Refinement in study design and analysis for health reform to address MCCs intends to advance the methodologies for common complex study designs used in health reform by modeling aspects of assessment, summarization, and adjustment for Multiple Chronic Conditions (MCCs). Persons with MCCs are increasing rapidly as a proportion of the population, and most previous research specifically excluded them from trial designs. With the advent of health reform aimed at improving population health, improving patient experience, and reducing inappropriate cost and utilization in health care, persons with MCCs are at the center of multiple evaluations since they are at higher risk to receive inappropriate, costly care and suffer adverse consequences. Two important elements of study design often involve persons with MCCs in health reform efforts such as the Patient-Centered Medical Home and Accountable Care Organizations: first, how to select those who are at risk for poor outcomes; and second, how to adjust models to predict how they would have done if there was no health reform intervention. Both require careful assessment and summarization of a person's MCCs to understand the risk. However, the sources of data about MCCs affect the accuracy, completeness, uncertainty, and temporality of risk, leading to mediocre predictions and errors in selection. We propose to use two large data sources called the Integrated Care Coordination Information System and the All-Payer All Claims system to assess the effect on study design from these problems and use advanced processing to improve patient selection and MCC summarization for risk adjustment in future trials. These two datasets, which have more than 490,000 patients and 2 million patients, respectively, are drawn from more than 6 different Electronic Health Record systems and more than 12 different claims and billing systems, allowing for comparisons of the relative quality and effect of MCC assessments and study design refinements. At the end of the study, we intend to make new metrics, algorithms, and heuristics available for researchers, policy-makers, and other stakeholders so that health reform studies can be improved and the needs of this vulnerable population can be better addressed.